These approaches aim to reduce the shift between the two domains, fine-tuning a deep network model by minimizing some divergence criteria between the source and the target data distributions. Indeed, the authors of [1]} demonstrated that fine-tuning can enhance the generalization capabilities of deep networks, mitigating the limitations characterizing the learned transferable features due to representation specificity. In particular, the fine-tuning operation aims to train a base network exploiting the labeled source data (labeled or unlabeled), and then directly re-use the first \(n\)  layers to carry out a target network using the target data, where the remaining layers are randomly initialized and trained using a discrepancy-based loss function. Depending on the size of the target dataset and on its similarity with the source one [2]}, the first \(n\)  layers of the target network can be frozen or fine-tuned during the training. In the end, the minimization of this divergence criterion between the source and the target data distributions aims to achieve a domain-invariant feature representation.
